This course introduces you to one of the main types of modeling families of supervised Machine Learning: Classification. You will learn how to train predictive models to classify categorical outcomes and how to use error metrics to compare across different models. The hands-on section of this course focuses on using best practices for classification, including train and test splits, and handling data sets with unbalanced classes.
Об этом курсе
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- 5 stars87,97 %
- 4 stars10,92 %
- 3 stars0,54 %
- 1 star0,54 %
Лучшие отзывы о курсе SUPERVISED MACHINE LEARNING: CLASSIFICATION
Great! Helps me build my career path in Data Science
Thank you Coursera.
Thank you IBM
Thank you to all instructors.
The course content is very great in the coding area and it is very helping. but a shortage that is clear is the theory behind every algorithm, the handling of it wasn't that much perfect.
Great course, well structured. The presentation of the different methods is very clear and well separated to understand the differences. A good understanding of classifiers is gained from this course.
Часто задаваемые вопросы
Когда я получу доступ к лекциям и заданиям?
Что я получу, оформив подписку на сертификацию?
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